Foamed concrete, a lightweight material with excellent thermal insulation and low density, is increasingly popular in construction. This study uses the discrete element method (DEM) to simulate the compressive behavior of foamed concrete, analyzing the effects of particle radius, porosity, and void distribution. The results highlight the important role of geometric and material parameters. Smaller particle radii improve packing density and strength, while a uniform void distribution maximizes compressive strength by minimizing stress concentration. This information provides a basis for optimizing the design of foamed concrete for better mechanical performance and wider applications in sustainable construction.

Cellular concretes combine low weight with good thermal performance, but their high porosity leads to brittle behavior and large variability, making parameter calibration challenging. In this study, a bonded-particle Concrete Particle Model (CPM) implemented in YADE is calibrated against uniaxial compression data of cellular concrete with porosity ≈ 0.7 and bulk density ≈ 598 kg m-3. Bayesian inference with Sequential Monte Carlo (SMC) is used to handle parameter uncertainty and model nonlinearity. A non-intrusive, physically informedprojector is introduced to constrain the tensile-to-elastic ratio k = σT /E and the crack-onset strain εcrack according to the material density. This approach improves convergence by filtering non-physical parameter sets before starting the simulation. The calibrated posteriors reproduce the experimental pre-peak stiffness, peak stress, and early softening. Young’s modulus and crack onset strain are well identified, while the friction coefficient remains weakly informed under uniaxial loading. Compared with an unconstrained baseline, the physically informed calibration reduces error and the number of required simulations, showing that simple physics-based constraints can significantly enhance DEM parameter calibration for cellular concretes